Existing electronic skin (e-skin) sensing platforms are equipped to monitor physical parameters using power from batteries or near-field communication. For e-skins to be applied in the next generation of robotics and medical devices, they must operate wirelessly and be self-powered. However, despite recent efforts to harvest energy from the human body, self-powered e-skin with the ability to perform biosensing with Bluetooth communication are limited because of the lack of a continuous energy source and limited power efficiency. Here, we report a flexible and fully perspiration-powered integrated electronic skin (PPES) for multiplexed metabolic sensing in situ. The battery-free e-skin contains multimodal sensors and highly efficient lactate biofuel cells that use a unique integration of zero- to three-dimensional nanomaterials to achieve high power intensity and long-term stability. The PPES delivered a record-breaking power density of 3.5 milliwatt·centimeter−2 for biofuel cells in untreated human body fluids (human sweat) and displayed a very stable performance during a 60-hour continuous operation. It selectively monitored key metabolic analytes (e.g., urea, NH4+, glucose, and pH) and the skin temperature during prolonged physical activities and wirelessly transmitted the data to the user interface using Bluetooth. The PPES was also able to monitor muscle contraction and work as a human-machine interface for human-prosthesis walking.

Feedback control theory has been extensively implemented to theoretically model human sensorimotor control. However, experimental platforms capable of manipulating important components of multiple feedback loops lack development. This paper describes WheelCon --an open source platform aimed at resolving such insufficiencies. Using only a computer, standard display, and inexpensive gaming steering wheel equipped with a force feedback motor, WheelCon safely simulates the canonical sensorimotor task of riding a mountain bike down a steep, twisting, bumpy trail. The platform provides flexibility, as will be demonstrated in the demos provided, so that researchers may manipulate the disturbances, delay, and quantization (data rate) in the layered feedback loops, including a high-level advanced plan layer and a low-level delayed reflex layer. In this paper, we illustrate WheelCon's graphical user interface (GUI), the input and output of existing demos, and how to design new games.In addition, we present the basic feedback model and the experimental results from our demo games, which align well with the model's prediction. The WheelCon platform can be downloaded at https://github.com/Doyle-Lab/WheelCon. In short, the platform is featured as cheap, simple to use, and flexible to program for effective sensorimotor neuroscience research and control engineering education.

This paper describes several surprisingly rich but simple demos and a new experimental platform for human sensorimotor control research and also controls education based on an off-the-shelf gaming platform. The platform safely simulates a canonical sensorimotor task of riding a mountain bike down a steep, twisting, bumpy trail using a standard display and inexpensive gaming steering wheel with a force feedback motor. We use the platform to verify our theory, presented in a companion paper, about how component hardware speed-accuracy tradeoffs (SATs) in control loops impose corresponding SATs at the system level, but also how effective architectures mitigate the deleterious impact of hardware SATs through layering and "diversity sweet spots (DSSs). Specifically, we measure the impacts on system control performance of disturbances and delays, quantization, and noise in feedback loops, both within the subjects nervous system and added externally via software in the platform. This provides a remarkably rich test of the theory, which is consistent with all preliminary data. Moreover, as the theory predicted, subjects effectively multiplex specific higher layer planning/tracking of the trail using vision with lower layer rejection of unseen bump disturbances using reflexes. In contrast, humans multitask badly on tasks that do not naturally distribute across layers (e.g. texting and driving). The platform is cheap and easy to build and use, and flexible to program for both research and education, yet highlights crucial gaps in both neuroscience and control theory that our new theory closes.

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LiDAR sensors are a powerful tool for robot simultaneous localization and mapping (SLAM) in unknown environments, but the raw point clouds they produce are dense, computationally expensive to store, and unsuited for direct use by downstream autonomy tasks, such as motion planning. For integration with motion planning, it is desirable for SLAM pipelines to generate lightweight geometric map representations. Such representations are also particularly wellsuited for man-made environments, which can often be viewed as a so-called "Manhattan world" built on a Cartesian grid. In this work we present a 3D LiDAR SLAM algorithm for Manhattan world environments which extracts planar features from point clouds to achieve lightweight, real-time localization and mapping. Our approach generates plane-based maps which occupy significantly less memory than their point cloud equivalents, and are suited towards fast collision checking for motion planning. By leveraging the Manhattan world assumption, we target extraction of orthogonal planes to generate maps which are more structured and organized than those of existing plane-based LiDAR SLAM approaches. We demonstrate our approach in the high-fidelity AirSim simulator and in realworld experiments with a ground rover equipped with a Velodyne LiDAR. For both cases, we are able to generate high quality maps and trajectory estimates at a rate matching the sensor rate of 10 Hz.